Agentic AI audit tools execute autonomously — planning, collecting evidence, generating findings — without mandatory human approval gates. Synalogic Assure does not. That distinction matters when a regulator asks how you validated the AI's work.
The core difference
AI generates draft findings, maps them to source evidence, and writes reports. Your team reviews and approves every output. The workflow cannot proceed without documented human sign-off. Every approval is timestamped, attributed, and permanently recorded.
AI proactively plans, requests evidence, tracks follow-ups, and generates reports without mandatory human approval gates. Humans review outputs but the workflow is not architecturally blocked pending sign-off. No documented evidence-to-finding chain.
"I checked it" is not a defence. A timestamped, source-verified approval record is.
Professional standards require auditors to exercise and document professional judgment — not delegate it to a tool that runs automatically.
Agentic AI is fast. It is also the wrong architecture for professionals whose judgment is regulated.
It proactively executes tasks — planning engagements, requesting evidence, generating findings, routing approvals — without waiting for human instruction at each step. The auditor reviews the output. The AI determined the path.
When a regulator, audit committee, or professional standards body asks how a finding was validated, the answer cannot be "the AI generated it and I reviewed the output." Professional accountability requires documented evidence that a qualified human exercised judgment — not just received a result.
Agentic AI audit tools — including Diligent AuditAI, the leading platform in this category — use language like "proactively executes," "keeps audits moving without manual coordination," "agentic automation" in their public marketing materials. This is a description of a tool designed to act on your behalf. The professional liability for the outputs of those actions remains with the practitioner. The question is whether you have the documented record to demonstrate you exercised professional judgment — not merely received a result.
Factual comparison across the dimensions that matter for regulated professionals.
| Synalogic Assure | Diligent AuditAI | |
|---|---|---|
| Core approach | AI validation platform — AI generates, humans must approve | Agentic AI — AI proactively executes tasks autonomously |
| Mandatory human sign-off | ✓ Architectural — cannot be bypassed | ✗ Not enforced in published product documentation — humans review output, AI proceeds |
| Source traceability | ✓ Every finding linked to specific source document | ✗ No publicly documented evidence-to-finding chain |
| Hard stop at findings | ✓ Citation review modal — workflow blocked until complete | ✗ No equivalent gate in published product documentation |
| Immutable audit trail | ✓ Who approved, what they saw, when — permanent | ✗ Activity logs documented — no approval-level audit trail described in product documentation |
| AI governance architecture | ✓ Patent-pending validation engine | ✗ No dedicated AI governance layer described in publicly available documentation |
| Time saving | ✓ 60–70% reduction in first-draft time | ~ 50–70% reduction in admin time (self-reported) |
| Data hosting | ✓ In-country, single-tenant | ✗ US-based, multi-tenant SaaS |
| AML/CTF compliance | ✓ Synalogic Vero — AusTrac Tranche 2 ready | ✗ Not available |
| Target market | Enterprise, regulated industries, internal audit functions | Agentic AI audit tools like Diligent AuditAI — typically Fortune 500, large enterprise, US/global GRC |
| Pricing model | Volume-driven, value-based — contact us | Custom quote, USD. AWS Marketplace lists Essentials tier at US$53,600/yr. Deployment fees of US$5,000–$25,000+ quoted separately (Vendr). 20%+ renewal increases documented in user reviews when not actively negotiated. |
| Ownership | Australian-owned | US-headquartered, NYSE-listed |
Pricing figures sourced from AWS Marketplace public listings, Vendr procurement analytics (vendr.com), and documented user reviews. All figures USD. Sources accessed April 2026. Synalogic pricing on request. Competitor information may change — verify directly with vendor.
Agentic AI tools lead with impressive automation statistics. Here is what a fair comparison actually looks like.
These are real efficiency gains. The speed is genuine. But it comes from an AI making decisions about what evidence to pull, what findings to generate, and how to frame risk — autonomously. The auditor reviews the output, not the process.
Comparable speed. Comparable coverage. The critical difference: every AI decision is subject to documented human review before it enters the audit record. The auditor controls the process, not just the outcome.
The speed argument does not favour agentic AI.
Both Synalogic Assure and leading agentic platforms deliver 60–70% faster audit engagements. Speed is not what separates them. What separates them is whether the auditor can prove — to a regulator, a professional standards body, or a client — that they exercised professional judgment on every finding. Only one architecture makes that proof possible.
Agentic AI audit tools are fast. But speed is not the same as completeness — and in audit, an incomplete evidence base creates professional exposure that appears in the report only as silence.
In an agentic audit tool, the AI autonomously determines which documents to request, which data to analyse, and which patterns to flag. The auditor reviews the findings. But those findings are the output of an evidence selection process the auditor did not control and cannot fully inspect.
An AI model trained on historical audit patterns will tend to look for what it was trained to recognise — and may not surface what falls outside that pattern. The audit appears complete. But the professional responsible cannot distinguish between "no issues found because there are none" and "no issues found because the AI didn't look there."
This is the structural risk of autonomous evidence selection: the AI's assumptions shape what it gathers, what it gathers shapes what it finds, and those findings appear to validate the original assumptions.
In Synalogic Assure, the auditor determines the engagement scope and the evidence the AI analyses. Evidence selection remains a professional judgment — the AI assists with it, but does not make it autonomously.
Every AI-generated finding links to the specific source it draws from. At the mandatory review gate, the auditor sees the finding and its evidence simultaneously — and decides whether the basis is sufficient before the output progresses.
This is not a slower approach. It is a more complete and defensible one — and in practice, it is just as fast.
The appropriate AI approach depends on what you are asking it to do. Not all audit-adjacent work carries the same professional liability profile.
Rules-based testing is the safest foundation — defined thresholds, policy checks, and transaction parameters running continuously against 100% of your data. AI is most valuable here for correlating signals and surfacing patterns that rule sets miss, not for making autonomous determinations. Synalogic Sentinel is purpose-built on this principle.
For engagements that produce findings professionals sign their names to, AI must accelerate professional work — not replace professional judgment. Evidence scope, sufficiency, and materiality require documented human reasoning. For internal audit at this standard, Synalogic Assure has no equivalent.
These are different functions that require different architectures. The best outcome combines both — on the same platform.
Threshold alerts, policy breach detection, and segregation of duties checks are well-suited to rules-based logic. The rules are transparent, predictable, and auditable. You know exactly what triggers an alert and why.
AI is valuable for correlating signals across monitoring streams — identifying patterns that rules alone would miss. But the monitoring logic itself should be defined and controlled by humans.
Synalogic Sentinel combines human-defined monitoring rules with AI-assisted signal correlation. The monitoring logic is transparent and controlled. AI enhances it — not replaces it.
Every alert requires documented human review before resolution. Signals from Sentinel flow directly into Assure engagements. The same validation standard throughout.
Assurance requires a structured engagement: scoped methodology, evidence gathered to that scope, professional judgment applied to findings, and a signed opinion that the professional is accountable for.
Continuous monitoring produces alerts. Synalogic Assure produces audited, validated, professionally defensible findings. These are not the same deliverable.
For true assurance outcomes, there is no better solution than Synalogic Assure.
Continuous monitoring closes the gaps between engagements. True assurance produces the defensible opinion. Both on the same platform, with the same validation standard.
Request a demoThe accountability gap is easiest to see on a real audit workflow. Request a demo and we'll show you the evidence review modal, the source traceability, and the approval trail — working on actual audit tasks.
Common questions from teams evaluating AI audit platforms.
The answer depends on what "best" means for your professional context. For speed and automation alone, several platforms — including DataSnipper, AuditBoard, and agentic AI tools like Diligent AuditAI — deliver meaningful efficiency gains. If the standard is speed plus professional accountability — meaning every AI-generated finding is traceable to source evidence, every validation decision is documented, and the auditor can prove they exercised professional judgment throughout — then Synalogic Assure is the only platform designed to meet that standard architecturally. For regulated organisations, government agencies, or any team where professional liability is a material concern, accountability is not separable from performance.
Yes — and this is the evidence integrity problem that most discussions of agentic AI in audit overlook. When an AI operates agentically, it makes decisions about what evidence to pull, what to include or exclude, how to weight signals, and how to frame risk — autonomously, before the auditor sees anything. The auditor reviews findings at the end. They do not review the AI's evidence selection decisions during the process. If the AI's selection logic is biased, incomplete, or systematically wrong, the audit reflects that bias. The auditor has approved a report shaped by decisions they didn't make and can't trace. This is not a problem with AI outputs — it is a problem with uncontrolled AI inputs to the audit.
Synalogic Assure delivers 60–70% faster audit engagement cycles — comparable to the efficiency gains claimed by leading agentic platforms. Speed is not the differentiating factor between Synalogic Assure and tools like DataSnipper, AuditBoard, or Diligent AuditAI. Both categories accelerate audit work meaningfully. The differentiator is what the auditor can prove after the engagement is complete: that every AI-generated finding was reviewed, validated, and approved by a named professional, against the specific source evidence that supports it. Synalogic Assure provides this documented record as an architectural output. Agentic platforms do not.
Continuous monitoring — automated alerts when thresholds are breached, policy violations detected, or anomalies identified — is a valuable control function. Rules-based monitoring is the safest architecture for this purpose: the logic is transparent, predictable, and controlled by humans. AI can assist by correlating signals across monitoring streams. Synalogic Sentinel does this. True assurance is a different function: it requires a structured engagement, scoped evidence gathering, professional judgment applied to findings, and a signed opinion the professional is accountable for. Continuous monitoring produces alerts. Assurance produces defensible findings. For true assurance activities, Synalogic Assure — with its enforced human validation and complete evidence trail — has no equivalent.
Competitor information on this page is based on publicly available product documentation, AWS Marketplace listings, Vendr procurement analytics, and documented user reviews, accessed April 2026. Synalogic makes no claim of affiliation with or endorsement by any third party named on this page. Pricing and product capabilities may change — verify current information directly with the relevant vendor. This page does not constitute legal or commercial advice. All competitor pricing figures are in USD.